Tag: Data Analytics

Latest Technologies and Future Trends by Top Key Players Forecast to 2030

Latest Technologies and Future Trends by Top Key Players Forecast to 2030

September 15, 2023

Several emerging technologies are poised to bring about a massive industry transformation as per reports. What is the forecast for future trends and the top key players till 2030? Here’s finding out.  Major Findings Here are some interesting findings related to technological advancements and technological disruptions throughout industries. It also offers insights into the future trends regarding emerging technologies.  Some Other Crucial Insights Here are a few other innovation forecast moot points for the period till 2030:  As can be seen, widespread transformation is at the core of business operations and efficiencies in the period till 2030. What the world is currently witnessing is a transitional phase with several emerging technologies being adopted by leading players in the Asia-Pacific and even worldwide. What is evident is that 2030 will push the bar well higher in terms of disruptions and eventual progress.  FAQs Some of the technologies that are already shaping the business landscape include automation and artificial intelligence, along with machine learning and IoT (Internet of Things). Other examples include data analytics and cloud computing along with blockchain technology. Organizations are steadily embracing these technologies to boost efficiency and offer more personalization to customers while also streamlining their internal operations or business processes. By 2030, the physical and digital worlds will also merge with technologies like AR, VR and 3D being used for creating digital twins in sectors like healthcare, manufacturing, real estate and more. There will also be a shift towards data native from cloud-native along with generative AI usage for closing up gaps between insights and data.  2. Who are the key players in these emerging technologies, and what are their roles in driving innovation? There are several key players for these emerging technologies from multiple standpoints. Countries like Japan, India, South Korea, and China are at the cusp of greater breakthroughs in terms of technological integration into the public and corporate spheres for greater efficiency, mitigation of risks, and many other purposes. At the same time, leading tech giants have a big role to play in terms of innovation and experimentation in order to drive future progress. The biggest players in these segments are chief technology officers or CTOs of companies. They have a vital role in terms of encouraging more innovation and building future technology blueprints for organizations.  There are a few challenges linked to the adoption of new technologies. These include legacy systems and perspectives, lack of training or skill sets, costs of new technologies and tools, and the speed of technological advancements, along with privacy concerns. The latter can be addressed through encryption measures, audits, and compliance with better regulations. Steady investments in up-skilling, training, and future-ready digital infrastructure are also the way forward with regard to tackling these challenges.  Several emerging technologies are poised to have a disruptive effect on various global sectors. Retail will witness a complete revamping of business strategies and models, becoming more personalized and data-driven with technological disruption. Industries like healthcare, manufacturing, insurance and finance should also witness major disruptions in the near future. 

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Addressing drug shortages with advanced analytics

Addressing drug shortages with advanced analytics

April 3, 2023

Drug shortages have become a part and parcel of modern healthcare systems due to several reasons. While there is a sizeable economic impact of drug shortages for manufacturers and pharmacies alike, there are widespread community and social disadvantages as well. Pharmacies or clinics running out of medicine stocks are representatives of a scenario that is often witnessed worldwide and with frightening consequences.  For example, Europe is already seeing shortages of commonly-used medicines. A survey by the Pharmaceutical Group of the European Union (EU) had 100% of 29 member nations reporting shortages of medicines amongst community pharmacists. 76% also stated how shortages had worsened than the earlier year (the survey was implemented between 14th November and 31st December 2022). The UK is also witnessing HRT shortages according to reports, while hospitals in the U.S. are also reporting issues with procurement for liquid ibuprofen, while ADHD diagnoses have gone up in the U.S. as well, leading to shortages of vital drugs for the same. Mexico is witnessing chronic shortages and unfulfilled prescriptions and supply fluctuations and disruptions have been seen widely throughout Asia too.  What are the reasons for medicine shortages?  Wondering about the reason for drug shortages? There are quite a few that can be noted in this context:  Higher seasonal illness outbreaks in the aftermath of COVID-19, leading to skyrocketing average annual demand for medicines that is higher than normal in several categories.  The inability of pharmaceutical companies to meet such unprecedented demand, with excess capacity restricted for cost control.  Global supply chain impact along with higher energy costs and inflation have impacted global drug manufacturers who have to contend with pricing measures.  Stockpiling by customers due to sudden drug shortages.  Over-prescribing by the system.  Reports estimate that the National Health Service in the UK loses a whopping 300 million pounds annually owing to partially-used or unused medication which cannot be reused or recycled.  Lack of systems for forecasting and identifying supply shortages, while ensuring proper inventory management.  Drug Shortage Solutions That May Work  There are a few drug shortage solutions that may be effective for combating and reducing shortages.  Data and analytics are enabling better access towards medicines worldwide while enabling superior supply and demand management for individual patients and pharmacies alike.  Real-time pharmacy, hospital, and clinical data will enable a proper understanding of the demand for specific drugs/medical products.  Leveraging electronic and public health records for enabling healthcare stakeholders to report demand figures for drugs, without revealing confidential patient data.  Opportunities for better inventory and supply chain management with AI (artificial intelligence) and machine learning (ML).  Generic entities may leverage smarter technologies for lowering manufacturing costs by up to 20% while enhancing production. Smarter and connected factories with proper insights and data analysis can enable higher savings and reliable deliveries.  Companies may look at higher procurement of local active ingredients while depending on go-to nations for the same. Boosting supply and production levels, along with harnessing real-time data analytics will enable tackling this scenario.  Supervised machine learning and analytics models can help in forecasting/predicting shortages for most drugs used throughout various categories, price points, and age groups.  Modelling can enable healthcare stakeholders to understand more about the issues behind drug shortages while analytics can also help predict demand for specific drugs based on historical data and current trends.  Pharmacies and other players may not have access to data on the supply side, although they have demand-side information. They will be able to gain more visibility into the supply chains of manufacturers with an integrated information-sharing system.  Data analytics-driven insights for optimizing orders and eventually lowering the effect of drug shortages on pharmaceutical and healthcare operations.  Systems for tracking and reporting drug shortages, including aspects like the frequency, drugs involved, period, causes, duration, managing strategies, impacts, and future shortages too.  Real-time identification and tracking of patients receiving shorter supplies of drugs by hospitals, clinics, and pharmacies. Immediate patient identification regulations for capturing present drug utilization across multiple categories.  Real-time identification and addressing situations along with finding out drugs in shorter supply. Predictive abilities enable higher time for researching material for alternative agents or making suitable arrangements for drug acquisition from other sites or facilities.  Once supply levels normalize for a drug, pharmacists and healthcare stakeholders may discontinue their surveillance regulations without waiting for technical assistance. Real-time data-filtering and reporting abilities are leveraged for viewing drug usage trends and prescription patterns throughout healthcare systems. These insights may enable higher standardization of drug management across institutions, while also facilitating better training of clinicians for lowering care variations.  Advanced data analytics will help address drug shortages and enable better inventory management simultaneously. However, suitable implementation, technological integration, and awareness are necessary for the same.  FAQs How can advanced analytics be used to address drug shortages? Advanced analytics can be deployed for tackling drug shortages through real-time tracking and surveillance of prescription trends and drug demand, forecasting shortages, and enabling better drug supply management.  What are the benefits of using advanced analytics to address drug shortages? Advanced analytics goes a long way towards helping tackle drug shortages, enabling forecasting future demand and shortages, identifying patterns for better management, and also enabling better global medicine access.  What are the challenges of using advanced analytics to address drug shortages? Challenges include technological integration, legacy systems integration, awareness regarding best practices, quality data generation, and more.  What are the best practices for implementing advanced analytics for drug shortage management? Best practices include unified and integrated public databases, suitable data modelling systems, suitable protocols for data security and privacy, and swift reporting mechanisms for demand and shortages.

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Data analytics plays a crucial role in clinical trial design and analysis by providing valuable insights into the effectiveness of new treatments and therapies.

The role of data analytics in clinical trial design and analysis

March 31, 2023

What is the role of data analysis in clinical trials? Can there be better clinical trial data analysis using R and other technologies? Is there a case for using big data analysis in clinical trials? Experts would certainly say Yes to all these questions. Clinical trials themselves have gone through sweeping changes over the last decade, with several new developments in immunotherapy, stem cell research, genomics, and cancer therapy among numerous segments. At the same time, there has been a transformation in the implementation of clinical trials and the process of identifying and developing necessary drugs.  To cite a few examples of the growing need for clinical trial data analysis, researchers gain quicker insights through the evaluation of databases of real-world patient information and the generation of synthetic control arms, while identifying drug targets alongside. They can also evaluate drug performance post-regulatory approvals in this case. This has lowered the cost and time linked to trials while lowering the overall burden on patients and enabling faster go-to-market timelines for drugs too.  What is driving data analysis in clinical trials?  Clinical trial data analysis is being majorly driven by AI (artificial intelligence) along with ML (machine learning), enabling the capabilities of collection, analysis, and production of insights from massive amounts of real-time data at scale, which is way faster than manual methods. The analysis and processing of medical imaging data for clinical trials, along with tapping data from other sources is enabling innovation of the entire process while being suitable for supporting the discovery procedure in terms of quickening the trials, go-to-market approaches, and launches.  The data volumes have greatly increased over the last few years, with more wearable usage, genomic and genetic understanding of individuals, proteomic and metabolomic profiles, and detailed clinical histories of patients derived from electronic health records. Reports indicate 30% of the data volumes of the world are generated by the global healthcare industry. The CAGR (compound annual growth rate) for healthcare data will touch 36% by the year 2025 as well. The volume of patient data in clinical systems has already grown by a whopping 500% to 2020 from 2016.  Data analysis in clinical trials- What else should you note?  Here are a few factors that are worth noting:  Synthetic control arm development  The role of data analysis in clinical trials is even more evident when one considers the development of synthetic control arms. Clinical drug discovery and trials may be fast-tracked while enhancing success rates and designs of clinical trials. Synthetic control arms may help in overcoming challenges linked to patient stratification and also lower the time required for medical treatment development. It may also enable better recruitment of patients through resolving concerns about getting placebos and enabling better management of diverse and large-sized trials.  Synthetic control arms tap into both historical clinical trials and real-world data for modelling patient control groups and doing away with the requirement for the administration of placebo treatments for patients which may hinder their health. It may negatively impact patient outcomes and enrolment in trials. The approach may work better for rare ailments where populations of patients are tinier and the lifespan is also shorter owing to the disease’s virulent nature. Using such technologies for clinical trials and bringing them closer to end-patients may significantly lower the overall inconveniences of travelling to research spots/sites and also the issue related to consistent tests.  ML and AI for better discovery of drugs ML and AI may enable a quicker analysis of data sets gathered earlier and at a swifter rate for clinicians, ensuring higher reliability and efficiency in turn. The integration of synthetic control arms in mainstream research will offer new possibilities in terms of transforming the development of drugs.  With an increase in the count of data sources including health apps, personal wearables and other devices, electronic medical records, and other patient data, these may well become the safest and quickest mechanisms for tapping real-world data for better research into ailments with sizeable patient populations. Researchers may achieve greater patient populations which are homogenous and get vital insights alongside. Here are some other points worth noting:  The outcomes of clinical trials are major metrics with regard to performance, at least as far as companies and investors are concerned. They are also the beginning of collaborations between patients, groups, and the healthcare sector at large. Hence, there is a clearly defined need for big data analysis in clinical trials as evident through the above-mentioned aspects.  FAQs How can data analytics be used in clinical trial design and analysis? Data analytics can be readily used for clinical trial design and analysis, expanding patient selection criteria, swiftly sifting through various parameters and helping researchers better target matching patients who match the criteria for exclusion and inclusion. Data analysis methods also enable better conclusions from data while also improving clinical trial design due to better visibility of the possible/predicted risk-reward outcomes.  What are the benefits of using data analytics in clinical trial design and analysis? The advantages of using data analytics in clinical trial design and analysis include the integration of data across diverse sources, inclusive of third parties. Researchers get more flexibility in terms of research, finding it easier to analyze clinical information. Predictive analytics and other tools are enabling swifter disease detection and superior monitoring.  What are the challenges of using data analytics in clinical trial design and analysis? There are several challenges in using data analytics for the analysis and design of clinical trials. These include the unavailability of skilled and experienced resources to implement big data analytics technologies, data integration issues, the uncertainty of the management process, storage and quick retrieval aspects, confidentiality and privacy aspects and the absence of suitable data governance processes.  What are the best practices for implementing data analytics in clinical trial design and analysis? There are numerous best practices for the implementation of data analytics for the analysis and design of clinical trials. These include good clinical data management practices, clinical practices, data governance

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Digital Behavior Analytics in Insurance

Why Digital Behaviour Analytics Should Be A Top Priority For The Insurers

March 14, 2023

Behaviour analytics in insurance is steadily gaining ground, with the steady evolution of consumer demands and an increasing focus on more flexibility and personalisation of offerings. User behaviour analytics is crucial for helping insurance companies meet varying and evolving requirements better while gaining invaluable insights in the process. Predominant user behaviour analytics software tools enable data analytics in a more specific form for the prediction and understanding of the habits of consumers.  How Behaviour Analytics In Insurance Are Beneficial And Other Vital Aspects   Predictive analytics of consumer behaviour enables diverse use cases for insurers, ranging from customised services to taking measures to combat fraud. Insurers usually use predictive analytics customer behaviour for gaining newer insights into consumer habits and offering more personalised services including things like recommendations, cross-selling new offerings, and lower premiums for safer drivers or healthy customers, or even healthy living tips for reducing claims in the future.  These are only a few examples of the usage of behaviour analytics in insurance.  Not for nothing has the user behaviour analytics market witnessed growth by leaps and bounds. This technology can be spread throughout the entire value chain by insurance companies and it is fast becoming a priority.  Along with smoother implementation and the right software tools, the importance of proper behavioural analytics security is also a focus point for insurance companies.  This is important since there is a huge volume of confidential data that is being gathered and analysed across segments. Hence, ensuring proper security is necessary at multiple levels.  Customers are now looking for more customised experiences with their insurers. 1/5th of insurance buyers reportedly state how their insurers do not provide any personalisation although 80% of them want the same.  This has been outlined in a DataArt report that takes information from Youbiquity Finance. At the same time, 77% of people surveyed in the report stated that they were eager to exchange behavioural information for getting customised services.  Some More Reasons And Use Cases For Behavioural Analytics In Insurance  The biggest reason for leveraging behavioural analytics in insurance is that customers are now looking for more flexibility, control, transparency, and customisation according to industry experts.  They want a scenario where their insurance costs are reflective of their specific behaviours and wish to tailor their insurance plans to their lifestyles.  For instance, if a consumer is medically in prime condition, then he/she will want this aspect to be reflected in premiums for policies.  Automotive insurance has been a great hunting ground for testing behavioural analytics for many insurance companies. Telematics devices in vehicles have helped generate data which is enabling price reductions and other benefits.  Life insurance is another category where customers are looking at evolving coverage amounts and controllable tenures.  Behavioural analytics is already helping people re-evaluate their requirements on a regular basis. Insurance companies will be able to tap these analytics to identify higher-risk consumers while meeting market requirements.  Global trends indicate how 5% of patients account for almost half of spending on healthcare. Hence, predictive analytics will play a crucial role in helping insurance companies identify risk factors for patients before these cases turn problematic.  These analytics can also enable firms to evaluate the regular activities of policyholders and responses in order to judge the various risks faced by them.  This will help in the removal of activities that might otherwise lead to premium increases for policies. Insurance companies can also move towards a more advisory role that is tailored toward the interests of the consumer. These analytics may also help prevent the occurrence of claims in many cases.  Behavioural analytics has been successful with regard to reducing losses, understanding customer interactions and networks within the ecosystem, and propensity modeling. It has also helped cross-sell various offerings along with up-selling whenever the time is ripe. It has also enabled insurance companies to swiftly offer assistance to customers at the time of claims and in other scenarios as well.  Hence, these benefits make a compelling case for the usage of user behaviour analytics by insurance firms.  FAQs What is digital behaviour analytics? Digital behaviour analytics is a specific form of data analytics that measures the user habits of consumers. It tracks consumer activity and interactions, along with their behavioural patterns in order to identify their needs, risks, and offer them more personalised solutions.  Why is digital behaviour analytics important for insurers? Insurers benefit from using digital behaviour analytics, since they can identify high-risk customers and instances while combating fraud and lowering claims and losses. They can also personalise their products and recommendations for consumers, giving them tailored solutions for various needs. At the same time, insurers can use these analytics to cross-sell/up-sell along with adopting an advisory role for customers.  What types of data can be analysed using digital behaviour analytics? Various types of data can be analysed through digital behaviour analytics. This includes customer interactions and activities throughout social media platforms and on the internet, along with their activity across various sites and applications. In-store, web-browsing, survey, advertising, and customer service data can also be analysed, to name a few sources. 

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omnichannel customer experience analytics

What Is Omnichannel Customer Experience Analytics, And How Should You Use It?

February 27, 2023

Omnichannel customer experience analytics are steadily gaining ground across the business spectrum, particularly in an environment where customer journey mapping is even more crucial for understanding how to fulfill prevalent requirements and personalize experiences.  Omnichannel is the way forward for businesses these days, combining online shopping and engagement channels with offline stores and other experience centers.  With the continued evolution of communication methodologies across industry segments, customers are now demonstrating interest in multi-channel or omnichannel business interactions.  Omnichannel customer experience analytics has grown to be a specific solution that helps companies leverage indispensable customer insights. This enables the collection and analysis of data from several channels including call center engagement, social media platforms, e-mails, and more.  Along with suitable customer data management and user feedback management, companies can analyze all this valuable data from multiple sources simultaneously, gaining insights and context which is not possible upon the analysis of only one data source.  Such dynamic user data analytics helps companies get decision-making inputs and actionable insights with a view toward ensuring better services for customers, developing newer products, expanding markets, and a lot more.  Why Omnichannel Customer Experience Analytics Are Important Tracking customer interactions and engagement throughout several channels is a key priority for most companies in recent times.  At the same time, they can maximize their value through this system of simultaneous analysis across sources.  From analyzing satisfaction and predicting customer behavior to understanding preferences, gaps in satisfying customers, the scope of new products and services, and geographical expansion or consolidation possibilities, the sky is the limit once data is analyzed across all touch points in the journey of the consumer.  This naturally makes engagement simpler while ensuring that companies get better visibility into the effectiveness and results of their marketing campaigns and outreach strategies.  It also helps enhance business revenues along with ensuring higher customer retention, loyalty, and conversions alike. Here are some key points worth noting in this regard:  Information is consolidated and made shareable throughout several channels, enabling better operational systems for reps and other personnel, saving their time and effort greatly.  It also encourages higher customer conversions by lowering the effort required to complete transactions.  The team can lower operating expenditure while tracking marketing spends closely.  AI may be leveraged to get more intuitive and valuable insights from organizational data.  With proper customer journey mapping¸ marketing processes can be better optimized along with tracking the impact of each channel on the end-consumer. Marketing strategies can be aligned better with the interests of consumers while personalizing marketing resources to suit their individual requirements.  Omnichannel analytics can help companies forecast inventory accurately along with combating diverse supply chain problems and logistics hurdles.  Analytics tools can also help find compliance and regulatory problems, along with possible organizational threats.  With the rapid evolution of buying habits of consumers globally, organizations will have to analyze newer mechanisms for doing business, with a focus on relevance and effective marketing and outreach.  It is here that omnichannel analytics becomes an invaluable tool for companies at every level.  FAQs What is omnichannel customer experience analytics?  Omnichannel customer experience analytics is a specialized form of analytics that gathers and analyzes customer data throughout multiple sources simultaneously, helping organizations derive crucial and actionable insights.  What is the purpose of using omnichannel customer experience analytics?  Omnichannel customer experience analytics helps companies boost customer experiences, understand pain points and gaps in service, plan expansion or the introduction of newer products/services in response to market demand, and also demystify customer personas and engagement, while also tracking how effective marketing campaigns are.  How can you use omnichannel customer experience analytics to improve customer experience? These tools can help companies gather data on the aspects that consumers are satisfied by and areas that require improvement as far as their experience is concerned. These insights help companies simplify operational processes and experiences for customers based on segmented and specific feedback.

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